clear
%% Problem
%   'EqualMinima','Himmelblau','Sixhump','ModifiedRastrigin','Vincent',
%   'Shubert','Composition','IncreasingMinima','Rastrigin','Schaffer'.
ProblemSet.FuncType = 'ModifiedRastrigin';
ProblemSet.dimension = 2;
ProblemSet.k = [3,4];
% ProblemSet.BaseFunc = 3;

[d, x_bound, ObjFunc, OptSet, precision_init, precision, ~, MaximalBudget, FileName]...
    = problem_setting(ProblemSet);

delta_candidate = [1,2,3,5,10,20,30,50,100];
iepsilon = 4;
epsilon = [0.1,0.01,0.001,0.0001];
OptSet.epsilon = epsilon(iepsilon);

%% problem
Problem.Dimension = d;
Problem.Domain = reshape(x_bound,[1,d*2]);
Problem.Sampling = @(n, region)Sampling_BoxConstraint(n, region, ObjFunc);
NewRegionNum = 2;
MaxPartitionDepth_d = ceil(log((x_bound(2,:)-x_bound(1,:))./precision_init)./log(NewRegionNum));
MaxPartitionDepth = sum(MaxPartitionDepth_d);
Problem.Partition = @(Region,SampleSet)Partition_BoxConstraint(Region, NewRegionNum, MaxPartitionDepth, SampleSet);
%% Algorithm
AlgorithmM.n0 = 4;
AlgorithmM.QuantileLevel = 0.3;
AlgorithmM.MaxSampleSize = 10;
AlgorithmM.StopCriteria = [1,MaximalBudget];
ClearRadius0 = (x_bound(2,:)-x_bound(1,:))./(NewRegionNum.^(MaxPartitionDepth_d));
ClearRadius = 2*min(ClearRadius0); % the maximum distance within a non-partitionable region
AlgorithmM.radius = ClearRadius;
AlgorithmM.local_search = @(starts,step,MaximalBudget)LocalSearch_test(ObjFunc,x_bound,starts,step,precision(iepsilon)/2,OptSet,MaximalBudget);
%% OPTIMIZATION
rep = 10;
OptObtained = zeros(rep,length(delta_candidate));
TotalBudget1 = zeros(rep,length(delta_candidate));
TotalBudget2 = zeros(rep,length(delta_candidate));
OptNoFound = zeros(rep,length(delta_candidate));
Times = zeros(rep,length(delta_candidate));
OptRemain = cell(rep,length(delta_candidate));
%%
for j = 1:length(delta_candidate)
    AlgorithmM.NewBudget = delta_candidate(j);
    for i=1:rep
        disp(['delta: ',num2str(delta_candidate(j)),', replication: ',num2str(i),'.'])
        tic
        [optima, ~, SampleSet, ~, ls_budget, OptSetRemain]...
            = prsmmo_ls_test( Problem, AlgorithmM, OptSet );
        Times(i,j) = toc;
        disp(['time used: ',num2str(Times(i,j)),'.'])
        OptObtained(i,j) = size(optima,1);
        TotalBudget2(i,j) = ls_budget;
        TotalBudget1(i,j) = size(SampleSet,1) - ls_budget;
        OptRemain{i,j} = OptSetRemain;
        OptNoFound(i,j) = size(OptSetRemain,1);
    end
end
%%
clear i j iepsilon OptSetRemain SampleSet ls_budget optima
save(FileName)
a=[mean(TotalBudget1)',mean(TotalBudget2)',mean(TotalBudget1+TotalBudget2)',var(TotalBudget1+TotalBudget2)'];
%% figures
figure()
hold on
yyaxis left
h1 = plot(delta_candidate,mean(TotalBudget1+TotalBudget2,1),'-o');
plot(delta_candidate,mean(TotalBudget1+TotalBudget2,1)+norminv(0.975,0,1)*sqrt(var(TotalBudget1+TotalBudget2,[],1)/rep),'--')
plot(delta_candidate,mean(TotalBudget1+TotalBudget2,1)-norminv(0.975,0,1)*sqrt(var(TotalBudget1+TotalBudget2,[],1)/rep),'--')
ylabel('The required budget size')
yyaxis right
h2 = plot(delta_candidate,mean(Times,1),'-^');
plot(delta_candidate,mean(Times,1)+norminv(0.975,0,1)*sqrt(var(Times,[],1)/rep),'--')
plot(delta_candidate,mean(Times,1)-norminv(0.975,0,1)*sqrt(var(Times,[],1)/rep),'--')
ylabel('The computational time [seconds]')
hold off
legend([h1,h2],{'Budget size','Computational time'})
xlabel('','Interpreter','latex','String','$\Delta$ value')
set(gca,'Fontname','Times New Roman','FontSize',16);
